Iterative Qubits Management for Quantum Index Searching in a Hybrid System

• URL: http://arxiv.org/abs/2209.11329v1
• Date: Thu, 22 Sep 2022 21:54:28 GMT
• Title: Iterative Qubits Management for Quantum Index Searching in a Hybrid System
• Authors: Wenrui Mu, Ying Mao, Long Cheng, Qingle Wang, Weiwen Jiang, Pin-Yu Chen
• Abstract summary: IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
• Score: 56.39703478198019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover's algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover's algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%.

Related papers

• Resource-Efficient and Self-Adaptive Quantum Search in a Quantum-Classical Hybrid System [24.67144593838334]
  We introduce ReSaQuS, a resource-efficient index-value searching system within a quantum-classical hybrid framework. We show that ReSaQuS has demonstrated a substantial reduction, up to 86.36% in cumulative qubit consumption and 72.72% in active periods.
  arXiv  Detail & Related papers  (2024-05-07T17:00:19Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• QuBEC: Boosting Equivalence Checking for Quantum Circuits with QEC Embedding [4.15692939468851]
  We propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques. Our proposed methodology reduces verification time on certain benchmark circuits by up to $271.49 times$.
  arXiv  Detail & Related papers  (2023-09-19T16:12:37Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Hybrid quantum-classical algorithms and quantum error mitigation [0.688204255655161]
  Google recently achieved quantum supremacy by using a noisy intermediate-scale quantum device with over 50 qubits. This article reviews the basic results for hybrid quantum-classical algorithms and quantum error mitigation techniques.
  arXiv  Detail & Related papers  (2020-11-02T23:34:22Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via Cost-Effective Uncomputation [7.92565122267857]
  We present a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse. Our results show that SQUARE improves the average success rate of NISQ applications by 1.47X.
  arXiv  Detail & Related papers  (2020-04-18T06:34:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.